A Novel Sparse-Aperture ISAR Imaging Algorithm Based on The Total Deep Variation Network

Abstract:
Sparse Aperture (SA) ISAR imaging, which relies on sparse prior knowledge to solve optimization problems with missing data, has gained much attention in recent years. However, most sparse ISAR imaging methods that assume the target reflectivity is sparse in the spatial domain are insufficient to capture the surface-like features of the target if no sparse representations are found for it and well incorporated in the imaging process. In order to capture the continuous nature of real target surfaces while exploiting sparsity in a tailored latent space, we introduced a Total Deep Variation network (TDV) to improve the performance of ISAR imaging with sparse aperture. Firstly, we utilize prior knowledge on bounded Total Variation (TV) to constrain the optimization-based imaging. Secondly, convolutional layers and activation functions have been used to implement the TV regularization and expand the TV regularization-based ISAR imaging into a cascade network, which automatically adjusts the free parameters of the optimization problem. To verify the effectiveness and superiority of the proposed method, we compared the performance on measured data with different methods. The results show that the Total Deep Variation network can perform lower reconstruction errors and higher resolution than traditional methods.